Stores competing with each other
Your stores that sit within 1 km of each other, with the people around them.
What it does
Every month: your stores that have another of your stores within 1 km, with how many people live around each, and how much they overlap from xMap AI, so you can see where branches split the same customers.
In one sentence: Monthly on the 1st at 08:00, take your table, find the nearest of your points, keep those where store_2_km ≤ 1, count who lives around, work out new fields, score and rank them with xMap AI, then email the list to you and show them on a map.
Why it is useful
Two stores too close split the same customers. This flow shows where branches compete with each other before you open another.
Who it is for
Retail & franchise expansion. Expansion, real estate and network planning teams deciding where the next store or franchise goes. Every use case for retail & franchise
Best use cases
- A chain reviewing its network
- A franchisor protecting franchisees
- A bank planning branch closures
What each run gives you
- An email of close stores
- A map
The time it saves
Monthly on the 1st at 08:00. Doing the same work by hand takes about 180 minutes each time; it runs about 1 time a month, so it saves about 3 hours a month (an estimate). It is medium: 9 blocks.
The flow, block by block
- On the 1st of every monthTrigger · ScheduleStarts the flow on a timetable.
- My storesYour data · My dataBrings in a table you uploaded or a list you saved on the My data page: your stores, customers, target accounts or sites.
- My other stores nearbyWhere · Find nearestFor each place, finds the nearest of your own points from a CSV you upload, for example the nearest ambulance to each accident or the nearest branch to each new lead, and adds its name and the distance.
- Another store within 1 kmLogic · FilterKeeps only the items that match a rule.
- Who lives aroundxMap data · DemographicsWho lives in an area: the number of residents, men and women, and nine age groups, from xMap’s population grid (hexagons of about 0.1 km², about 100 countries).
- People per storeLogic · Calculated fieldAdds fields worked out from the others: a number, a text or a yes/no.
- Rate the overlap with xMap AIxMap AI · xMap LLMTell the AI what to do in plain words.
- Email me the listAction · EmailSends one email per run with all the items, or one email per item.
- Show them on a mapAction · Show on a mapShows the results as points on a map, with the area of interest.
Every block can be changed after you use the template: the city, the type of place, the score, where the results go.
Use it now
Answer a few questions, enter your work email, and it runs in your own account, kept running until you pause it. Use this template.
How to set it up
- Build your first flow in ten minutes: From a blank page to a flow that emails you a ranked list every morning.
- Find the nearest of your own sites: Match each result to its closest branch, depot or vehicle.
- Ask the map a question: How many of a kind of place, how many people for each, and where the gaps are, answered on the spot.
- Enrich a list once: A CSV of addresses or companies back with phone, website, rating and an AI score.
- How to score candidate store sites: Compare sites on residents, competitors and neighbours, with an AI score and a reason.
- Location intelligence: Using where things are, and what is around them, to make business decisions.
- Site selection: Choosing where to open a store, branch, clinic or facility.
More for Retail & franchise expansion
- Expansion committee pack, every month: A monthly expansion committee pack: every block scored, the best 20 investigated, approved, mapped.
- Score every neighbourhood for a new branch: Every block of your city scored for a new supermarket, the 20 best on a map.
- Where my rival is and I am not: Every rival branch with none of yours within 2 km, with who lives around it.
- New homes with no supermarket yet: New apartment buildings with no supermarket nearby, scored as places for your next store.
- Busy stations with no convenience store: The train and metro stations with the fewest convenience stores around them, scored for a new one.
- Where to put click and collect lockers: Blocks with many residents and no store near, for parcel lockers.